Ward Linkage Method Research Articles

Assessment of swallowing performance in patients with neurodegenerative disease: A hierarchical cluster analysis.

Swallowing is a complex process that alters with age and neurological diseases; swallowing disorders can be a consequence of both of them. As an advanced multivariate statistical method, hierarchical cluster analysis (HCA) was utilized to make the dendrograms, which was used to find the relationship between the variables. The purpose of this study is to ascertain the type of clustering exhibited by the variables using HCA and to evaluate the approach to major neurodegenerative diseases (MND) with swallowing disorders based on the results obtained. Data were collected from a total of 173 patients from various neurological diagnoses, such as dementia, Parkinson's disease, stroke and polyneuropathy, aging between 42 and 104 (mean of age 72.85) by using the Montreal Cognitive Assessment, the Edinburgh Feeding Evaluation Scale (EdFED), the Eating Assessment Tool (EAT-10), and the Modified Mann Swallowing Ability test. From the collected data, dendrograms were formed by using HCA with Ward linkage method. Based on cluster analysis results, clusters demonstrate statistical significance. They center around EdFED, EAT-10, and age in each MND. In healthy individuals, variables are not clustered as in the patient group. This study holds importance as it can give clinicians a different perspective on determining and managing the elderly population's swallowing problems. The HCA method explicitly proposes which variables should be examined concurrently in the clinic for MND. This research is one of the pioneering studies conducted by using the HCA method.

Endophenotypes of Primary Osteoarthritis of the Hip Joint in the Bulgarian Population over 60 Years Old.

Aim. To identify subgroups of patients with primary osteoarthritis of the hip joint (pHOA) with similar imaging and laboratory findings, disease evolution, and response to conventional therapies. Methods. We performed further statistical analyses on patient data from two published, double-blind, randomized, and placebo-controlled studies (DB-RCTs), which examined the effects of intra-articular corticosteroids (ia-CSs), hyaluronic acid (ia-HA)-KИ-109-3-0008/14.01.2014, and intravenous bisphosphonates (iv-BPs) -KИ- 109-3-0009/14.01.2014 compared to the country's standard pHOA therapy. The data span an 8-year follow-up of 700 patients with pHOA, including: 1. Clinical parameters (WOMAC-A, B, C, and T; PtGA). 2. Laboratory markers (serum calcium and phosphate levels; 25-OH-D and PTH, markers for bone sCTX-I and cartilage uCTX-II turnover). 3. Radiological indicators: X-ray stage (Kellgren-Lawrence (K/L) and model (Bombelli/OOARSI), width (mJSW), speed (JSN mm/year), and zone of maximum narrowing of the joint space (max-JSN)-determining the type of femoral head migration (FHM). 4. DXA indicators: bone geometry (HAL; NSA; and MNW); changes in regional and total bone mineral density (TH-BMD, LS-BMD, and TB-BMD). 5. Therapeutic responses (OARSI/MCII; mJSW; JSNmm/yearly) to different drug regimens (iv-BP -zoledronic acid (ZA/-5 mg/yearly for 3 years)); ia-CS 40 mg methylprednisolone acetate, twice every 6 months; and ia-HA with intermediate molecular weight (20 mg/2 mL × 3 weekly applications, two courses every 6 months) were compared to standard of care therapy (Standard of Care/SC/), namely D3-supplementation according to serum levels (20-120 ng/mL; target level of 60 ng/mL), simple analgesics (paracetamol, up to 2.0 g/24 h), and physical exercises. The abovementioned data were integrated into a non-supervised hierarchical agglomerative clustering analysis (NHACA) using Ward's linkage method and the squared Euclidean distance to identify different endophenotypes (EFs). Univariate and multivariate multinomial logistic regression analyses were performed to determine the impact of sex and FHM on clinical and radiographic regression of pHOA. Results. A baseline cluster analysis using incoming (M0) patient data identified three EFs: hypertrophic H-HOA, atrophic A-HOA, and intermediate I-HOA. These EFs had characteristics that were similar to those of patients grouped by radiographic stage and pattern ('H'-RPs, 'I'-RPs, and 'A'-RPs), p < 0.05). The repeated cluster analysis of M36 data identified four EF pHOAs: 1. Hypertrophic (slow progressors, the influence of the type of femoral head migration (FHM) outweighing the influence of sex on progression), progressing to planned total hip replacement (THR) within 5 (K/LIII) to 10 (K/LII) years. 2. Intermediate (sex is more important than the FHM type for progression) with two subgroups: 2#: male-associated (slow progressors), THR within 4 (K/LIII) to 8 years. (K/LII). 2* Female-associated (rapid progressors), THR within 3 (K/LIII) to 5 (K/LII) years. 3. Atrophic (rapid progressors; the influence of FHM type outweighs that of sex), THR within 2 (K/LIII) to 4 (K/LII) years. Each EF, in addition to the patient's individual progression rate, was also associated with a different response to the aforementioned therapies. Conclusions. Clinical endophenotyping provides guidance for a personalized approach in patients with pHOA, simultaneously assisting the creation of homogeneous patient groups necessary for conducting modern genetic and therapeutic scientific studies.

Wetland vegetation composition and ecology of Lake Abaya in southern Ethiopia.

Wetland vegetation and ecology of Lake Abaya in the southern Ethiopia was studied to determine floristic composition, plant community type and vegetation ecology. A total of 102 plots were laid along transects that were set up preferentially across areas where there were rapid changes in vegetation or marked environmental gradients to collect data on estimate of percentage aerial cover of plant species and environmental variables. Vegetation data was analyzed by agglomerative hierarchical cluster analysis using similarity ratio as a resemblance index and Ward's linkage method. Multivariate data analysis was performed using appropriate packages in R version 2.14.0. Canonical Correspondence Analysis (CCA) was used to explore the relationship between the species composition and environmental variables. The environmental data included in the CCA were determined using stepwise backward and forward selection of variables by ANOVA test. Statistical measurement regarding species diversity, richness and evenness of the plant community types was carried out by using Shannon-Wiener diversity indices. A total of 92 plant species belonging to 66 genera and 34 families were identified. Families Poaceae, Asteraceae, Fabaceae, Cyperaceae, Solanaceae, Euphorbiaceae and Amaranthaceae account for about 56.99% of the total proportion. Based on the cluster analysis, five plant community types were identified. The most important factors influencing the plant species composition and pattern of wetland plant communities were water drainage, water depth, land use, slope, altitude, and hydrogeomorphology. Therefore, these factors should be considered in future management and protection under the circumstance of climate change and human activities.

Identifying Biomarkers Using Support Vector Machine to Understand the Racial Disparity in Triple-Negative Breast Cancer.

With the properties of aggressive cancer and heterogeneous tumor biology, triple-negative breast cancer (TNBC) is a type of breast cancer known for its poor clinical outcome. The lack of estrogen, progesterone, and human epidermal growth factor receptor in the tumors of TNBC leads to fewer treatment options in clinics. The incidence of TNBC is higher in African American (AA) women compared with European American (EA) women with worse clinical outcomes. The significant factors responsible for the racial disparity in TNBC are socioeconomic lifestyle and tumor biology. The current study considered the open-source gene expression data of triple-negative breast cancer samples' racial information. We implemented a state-of-the-art classification Support Vector Machine (SVM) method with a recurrent feature elimination approach to the gene expression data to identify significant biomarkers deregulated in AA women and EA women. We also included Spearman's rho and Ward's linkage method in our feature selection workflow. Our proposed method generates 24 features/genes that can classify the AA and EA samples 98% accurately. We also performed the Kaplan-Meier analysis and log-rank test on the 24 features/genes. We only discussed the correlation between deregulated expression and cancer progression with a poor survival rate of 2 genes, KLK10 and LRRC37A2, out of 24 genes. We believe that further improvement of our method with a higher number of RNA-seq gene expression data will more accurately provide insight into racial disparity in TNBC.

Comparison of the Use of Linkage in Cluster Integration With Path Analysis Approach

This study aims to compare integrated clusters on various linkages (Ward, Complete, and Average linkage) with path analysis to classify the behavior of subservient to pay for mortgages. The data used are primary data from questionnaires filled out by Bank X customers, using purposive sampling. The measurement method used is the average score method. Liu et al. with the title “Enhanced path-based Clustering Algorithm” shows the results that path analysis with cluster integration gives results that outperform other cluster algorithms. So, this study uses the cluster integration with path analysis method by comparing the linkage. The results showed that cluster integration in the Ward linkage method with path analysis was the best method, by comparing the coefficient of determination. The variables of attitude, subjective norms, behavioral control, and intention to comply with paying explained the behavior of Bank X customers to comply with paying mortgages by 88.32%, while the remaining 11.68% was explained by variables outside the model.

Identifying and validating subtypes of Parkinson's disease based on multimodal MRI data via hierarchical clustering analysis.

ObjectiveWe wished to explore Parkinson's disease (PD) subtypes by clustering analysis based on the multimodal magnetic resonance imaging (MRI) indices amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV). Then, we analyzed the differences between PD subtypes.MethodsEighty-six PD patients and 44 healthy controls (HCs) were recruited. We extracted ALFF and GMV according to the Anatomical Automatic Labeling (AAL) partition using Data Processing and Analysis for Brain Imaging (DPABI) software. The Ward linkage method was used for hierarchical clustering analysis. DPABI was employed to compare differences in ALFF and GMV between groups.ResultsTwo subtypes of PD were identified. The “diffuse malignant subtype” was characterized by reduced ALFF in the visual-related cortex and extensive reduction of GMV with severe impairment in motor function and cognitive function. The “mild subtype” was characterized by increased ALFF in the frontal lobe, temporal lobe, and sensorimotor cortex, and a slight decrease in GMV with mild impairment of motor function and cognitive function.ConclusionHierarchical clustering analysis based on multimodal MRI indices could be employed to identify two PD subtypes. These two PD subtypes showed different neurodegenerative patterns upon imaging.

An Analysis of Different Distance-Linkage Methods for Clustering Gene Expression Data and Observing Pleiotropy: Empirical Study

Background Large amounts of biological data have been generated over the last few decades, encouraging scientists to look for connections between genes that cause various diseases. Clustering illustrates such a relationship between numerous species and genes. Finding an appropriate distance-linkage metric to construct clusters from diverse biological data sets has thus become critical. Pleiotropy is also important for a gene’s expression to vary and create varied consequences in living things. Finding the pleiotropy of genes responsible for various diseases has become a major research challenge. Objective Our goal was to establish the optimal distance-linkage strategy for creating reliable clusters from diverse data sets and identifying the common genes that cause various tumors to observe genes with pleiotropic effect. Methods We considered 4 linking methods—single, complete, average, and ward—and 3 distance metrics—Euclidean, maximum, and Manhattan distance. For assessing the quality of different sets of clusters, we used a fitness function that combines silhouette width and within-cluster distance. Results According to our findings, the maximum distance measure produces the highest-quality clusters. Moreover, for medium data set, the average linkage method, and for large data set, the ward linkage method works best. The outcome is not improved by using ensemble clustering. We also discovered genes that cause 3 different cancers and used gene enrichment to confirm our findings. Conclusions Accuracy is crucial in clustering, and we investigated the accuracy of numerous clustering techniques in our research. Other studies may aid related works if the data set is similar to ours.

Pattern Recognition for Steam Flooding Field Applications Based on Hierarchical Clustering and Principal Component Analysis.

Steam flooding is a complex process that has been considered as an effective enhanced oil recovery technique in both heavy oil and light oil reservoirs. Many studies have been conducted on different sets of steam flooding projects using the conventional data analysis methods, while the implementation of machine learning algorithms to find the hidden patterns is rarely found. In this study, a hierarchical clustering algorithm (HCA) coupled with principal component analysis is used to analyze the steam flooding projects worldwide. The goal of this research is to group similar steam flooding projects into the same cluster so that valuable operational design experiences and production performance from the analogue cases can be referenced for decision-making. Besides, hidden patterns embedded in steam flooding applications can be revealed based on data characteristics of each cluster for different reservoir/fluid conditions. In this research, principal component analysis is applied to project original data to a new feature space, which finds two principal components to represent the eight reservoir/fluid parameters (8D) but still retain about 90% of the variance. HCA is implemented with the optimized design of five clusters, Euclidean distance, and Ward’s linkage method. The results of the hierarchical clustering depict that each cluster detects a unique range of each property, and the analogue cases present that fields under similar reservoir/fluid conditions could share similar operational design and production performance.

Fruit-related lifestyles as a segmentation tool for fruit consumers

PurposeThis study develops a modified food-related lifestyle (FRL) instrument to analyse Kosovo consumers' fruit consumption behaviour and attitudes.Design/methodology/approachThe research study is based on a structured questionnaire designed using a reduced version of the FRL instrument, including evaluation factors related to fruit consumption, which is useful to describe a fruit-related lifestyle. Data were collected through a face-to-face survey with 300 consumers in three main cities in Kosovo. A principal component analysis (PCA) with Varimax rotation and Kaiser Normalisation was performed to interpret and investigate fruit-related lifestyles. Cluster analysis was performed to analyse market segments, using the identified factors obtained from the PCA, a hierarchical clustering algorithm with a Ward linkage method and the K-means clustering technique.FindingsConsumption behaviour is motivated by health concerns (perceived), fruit (nutrition) content and consumption habits. Four distinct consumer clusters were identified based on the fruit-related lifestyle instrument and analysed considering the different fruit purchase and consumption behaviour, attitudes towards health, quality, taste and safety.Research limitations/implicationsThe authors adapted a survey tool based on a reduced FRL instrument to elaborate a specific survey instrument suitable to describe the fruit-related consumer's lifestyles. The instrument was not designed according to the standard scales design procedure, but it is a first step towards creating a fruit-related lifestyle instrument.Originality/valueThe fruit-related lifestyle instrument can be used in studies focused on fruit consumer segmentation. Results provide insight into fruit marketing and distribution companies, which can adjust their marketing strategies and customer-oriented initiatives tailored for specific consumer segments. Results can be useful also for policymakers to promote increased fruit consumption.

Spectral Reflectance-Based Mangrove Species Mapping from WorldView-2 Imagery of Karimunjawa and Kemujan Island, Central Java Province, Indonesia

Mangrove mapping at the species level enables the creation of a detailed inventory of mangrove forest biodiversity and supports coastal ecosystem management. The Karimunjawa National Park in Central Java Province is one of Indonesia’s mangrove habitats with high biodiversity, namely, 44 species representing 25 true mangroves and 19 mangrove associates. This study aims to (1) classify and group mangrove species by their spectral reflectance characteristics, (2) map mangrove species by applying their spectral reflectance to WorldView-2 satellite imagery with the spectral angle mapper (SAM), spectral information divergence (SID), and spectral feature fitting (SFF) algorithms, and (3) assess the accuracy of the produced mangrove species mapping of the Karimunjawa and Kemujan Islands. The collected field data included (1) mangrove species identification, (2) coordinate locations of targeted mangrove species, and (3) the spectral reflectance of mangrove species measured with a field spectrometer. Dendrogram analysis was conducted with the Ward linkage method to classify mangrove species based on the distance between the closest clusters of spectral reflectance patterns. The dendrogram showed that the 24 mangrove species found in the field could be grouped into four levels. They consisted of two, four, and five species groups for Levels 1 to 3, respectively, and individual species for Level 4. The mapping results indicated that the SID algorithm had the highest overall accuracy (OA) at 49.72%, 22.60%, and 15.20% for Levels 1 to 3, respectively, while SFF produced the most accurate results for individual species mapping (Level 4) with an OA of 5.08%. The results suggest that the greater the number of classes to be mapped, the lower the mapping accuracy. The results can be used to model the spatial distribution of mangrove species or the composition of mangrove forests and update databases related to coastal management.

CLASSIFICATION OF LIQUIDS USING A PATCH ANTENNA AND HIERARCHICAL CLUSTERING ALGORITHMS

Detection of hazardous liquids used in explosive production is important in terms of public safety and health. Because many threats can be prevented by detecting these liquids at security controls points. As existing methods have some disadvantages in terms of accuracy, practicality or reliability, there is a demand for new methods for hazardous liquid detection. In this paper, a circular patch antenna for hazardous liquid detection was designed and by connecting to a vector network analyzer a group of measurements was made. Then, this dataset was used by hierarchical clustering algorithms employed in this study to detect hazardous liquids. The results show that high classification accuracy can be achieved when Ward linkage method is preferred.

West Java local rice panicle branching architecture

As local varieties of rice have a very important role as a source of valuable traits in developing high yielding variety through plant breeding programs, it is needed to be characterized. Panicle branching characterization is one of the efforts to understand local varieties of rice characteristics more. We have observed thirtheen characters of panicle branching on 24 West Java local rice varieties. Five panicles of each varieties as accession was observed and statistical analysed. Tukey’s Honest Significant Difference (HSD) test showed differences among accessions in all panicle branching characteristics observed. Based on Principles Component Analysis (PCA), the panicle branching characters observed generally showed the same direction, but among them were not always to be correlated. In the result of clustering based on the ward linkage method, the accessions were divided into two clusters. The first one had 8 members, and the second one had 16 members. The cophenetic correlation coefficient was 0.60, indicated that the clustering through standardized value was faithfully enough to represent the original distances. The result of this research can provide the information for breeder in selecting rice genotypes which have more seeds per panicle as parent in assembling new high yielding rice varieties.

Symptom Experience Among Family Caregivers: Symptom Cluster Analysis

Family caregivers of older adults experience physical and psychological symptoms such as fatigue, insomnia, depression, or anxiety which may negatively impact their quality of life. Although those symptoms often co-occur, there is no prior research on symptom clusters identification among family caregivers. Symptom cluster analysis in chronic disease populations demonstrates usefulness in clinical management. An in-depth understanding of the pattern of symptom experience can guide intervention development to improve caregiving experience, coping skills, and well-being. This study aimed to identify symptom clusters as experienced by family caregivers in the US. We analyzed responses of 2,652 unpaid caregivers from the National Study of Caregiving (NSOC) III (2017) survey to identify underlying symptom clusters. The NSOC is a nationally representative survey of family caregivers to older adults with limited functions in daily activities. We used 17 binary items measuring the physiological and psychological symptoms of caregivers. We conducted a hierarchical cluster analysis with Ward linkage method to identify the symptom clusters. Three symptom clusters were identified based on the optimum silhouette width as follows: (1) Cluster 1 (lack of cheerfulness, peacefulness, and full of life, breathing problems, and sleep interrupted); Cluster 2 (feeling lonely, down, bored, nervous, worrisome, upset, and having little interest); and (3) Cluster 3 (limited strengths in arms and legs, having low energy, pain, and troubles falling back asleep). Our findings indicate that physical and psychological symptoms are highly preventable in family caregivers. Future research on symptoms management strategies can focus on targeting multiple symptoms based on their co-occurence.

Hierarchical cluster analysis of multimodal imaging data identifies brain atrophy and cognitive patterns in Parkinson’s disease

BackgroundParkinson's disease (PD) is a heterogeneous condition. Cluster analysis based on cortical thickness has been used to define distinct patterns of brain atrophy in PD. However, the potential of other neuroimaging modalities, such as white matter (WM) fractional anisotropy (FA), which has also been demonstrated to be altered in PD, has not been investigated. ObjectiveWe aim to characterize PD subtypes using a multimodal clustering approach based on cortical and subcortical gray matter (GM) volumes and FA measures. MethodsWe included T1-weighted and diffusion-weighted MRI data from 62 PD patients and 33 healthy controls. We extracted mean GM volumes from 48 cortical and 17 subcortical regions using FSL-VBM, and the mean FA from 20 WM tracts using Tract-Based Spatial Statistics (TBSS). Hierarchical cluster analysis was performed with the PD sample using Ward's linkage method. Whole-brain voxel-wise intergroup comparisons of VBM and TBSS data were also performed using FSL. Neuropsychological and demographic statistical analyses were conducted using IBM SPSS Statistics 25.0. ResultsWe identified three PD subtypes, with prominent differences in GM patterns and little WM involvement. One group (n = 15) with widespread cortical and subcortical GM volume and WM FA reductions and pronounced cognitive deficits; a second group (n = 21) with only cortical atrophy limited to frontal and temporal regions and more specific neuropsychological impairment, and a third group (n = 26) without detectable atrophy or cognition impairment. ConclusionMultimodal MRI data allows classifying PD patients into groups according to GM and WM patterns, which in turn are associated with the cognitive profile.

Assessment of Water Quality of Major Tributaries in Seoul using Water Quality Index and Cluster Analysis

Objectives : 17 water quality measurement networks (WQMNs, tributaries) in Seoul were analyzed by using NSFWQI and cluster analysis to provide basic data for future river water quality management so that citizens could easily and comprehensively understand the water quality information on the rivers in Seoul.Methods : For the past 3 years (2015~2017), in order to estimate WQI, 9 items, DO (% sat), Fecal coliform, pH, BOD, Temperature change (TC), TP, NO3-, Turbidity and Total solids, were selected from among the 19 water quality data measurement items produced monthly from 17 WQMNs in Seoul. WQI was derived and graded using NSFWQI and cluster analysis was performed using Ward Linkage Method, SOM (Self Organizing Map).Results and Discussion : Water quality of most water quality monitoring networks was BOD Ⅱ grade (slightly good) or higher and TP Ⅲ grade (normal) or higher according to the standard of water quality and water ecosystem river living environment, and NSFWQI was also 64 (Medium)~89 (Good). All showed good water quality. NSFWQI does not show a significant difference by season, so it is believed that it is affected by anthropogenic sources rather than seasonal effects. As a result of examining the correlation between NSFWQI and water quality level according to environmental standards, it was confirmed that R2 has a relatively good correlation with 0.78, and there is no clear difference between the two groups, and through this, it was found that the currently implemented water quality rating system and NSFWQI are well matched. As a result of cluster analysis using ward linkage method and SOM for 17 WQMNs, it was largely divided into 6 groups according to water quality characteristics.Conclusions : It is important to manage pollution sources to systematically manage river water quality as a water resource. It is therefore expected that by converting from the complicated and various water quality information such as is found in this study into a simple water quality index and grouping, the river water quality can be easily understood and can be utilized in the future as basic data for water quality management in Seoul.

Proteomics analysis of host cell proteins after immobilized metal affinity chromatography: Influence of ligand and metal ions

Different degrees of protein purity have been observed in immobilized metal affinity chromatography ranging from extremely high purity to moderate and low purity. It has been hypothesized that the host cell protein composition and the metal ligands are factors governing the purity of a protein obtained after immobilized metal affinity chromatography (IMAC). Ni nitrilotriacetic acid (NTA) has become the first choice for facile His-tagged protein purification, but alternative ligands such as iminodiacetic acid (IDA) with other immobilized metal ions such as Zn, Cu and Co are valuable options when the expected purity or binding capacity is not reached. Especially Cu and Zn are very attractive, due to their reduced environmental and safety concerns compared to Ni. Co and Zn are more selective than Ni and Cu. This increased selectivity comes at the cost of weaker binding. In this work, the influence of ligand choice on protein purity after IMAC was evaluated by several methods, including peptide mapping. His-tagged GFP was used as model protein. We found that host cell protein (HCP) content varies drastically between ligands, as IDA eluates generally showing higher HCP concentrations than NTA. The relative content of the key amino acids His, Cys and Trp in the sequence of the co-eluted protein does not suffice to explain co-eluting propensity. The co-elution of HCPs is mostly influenced by metal binding clusters on the protein surface and not by total content or surface concentration of metal interacting amino acids. Prediction of co-elution is not dependent on these clusters alone, due to protein-protein interactions, indicted by a relative low metal binding cluster score but high co-elution propensity and in a lot of cases these proteins are often part of complex such as ribosome and chaperones. The different co-eluting proteins were presented by a heatmap with a dendrogram. Ward's linkage method was used to calculate the distance between groups of co-eluting proteins. Clustering of co-eluting HCPs was observed according to ligand and by metal ions, with Zn and Co forming one cluster and Ni and Cu another. The co-elution of host cell proteins can be explained by clusters of metal interacting amino acids on the protein surface and by protein-protein interactions. While Ni NTA still appears to be highly advantageous, it might not be the cure-all for all applications.

Analysis of soil depth temperature changes in Iran according to classification of atmospheric circulation patterns

The purpose of this study was to investigate the relationship between atmospheric circulation patterns and soil depth temperature changes in Iran. To verify the correlation between atmospheric circulation patterns and soil temperature changes in the country, ten synoptic stations in the geographical range of Iran representing different climates were selected for research and analysis of data. To extract the circulation patterns by cluster analysis using Ward linkage method, nine circular clusters were identified and extracted. Among the patterns, five patterns were identified during the cold season, two patterns during the warm period, and two patterns during the transition period. Soil temperature data were first deseasonalized and changes (increase and decrease) due to the presence of warm and cold season were removed. The results of the correlation analysis between soil temperature changes in the nine-dimensional or nonuple patterns showed that there was no significant correlation between the mentioned patterns and temperature changes in the studied stations in models No. 1 and No. 2. The results of correlation analysis also showed that there was significant correlation between circulation patterns and soil temperature changes in seven other circulation patterns. The results of correlation analysis also showed that among patterns of circulation, patterns 7, 4, and 9 had the most impact on soil temperature changes in the country.

PIN150 ANALYSIS OF TREATMENT SEQUENCES FROM THE FRENCH NATIONAL SNIIRAM DATABASE: CASE STUDY OF INCIDENT PEOPLE LIVING WITH HIV IN 2013

The study purpose was to describe the changes and discontinuations of antiretroviral combination therapy (cART) (number of treatments, occurrence, duration) over the cohort of incident people living with HIV (PLWHIV) in 2013, included in the French national Health Insurance database (SNIIRAM). It also aims at experimenting data mining methods to study treatment sequences. From the SNIIRAM, we extracted PLWHIV affiliated to the general insurance scheme (N=96,423) through specific chronic diseases status and/or reimbursement of HIV laboratory tests and/or HIV-related hospitalizations and/or reimbursements of antiretroviral drugs in 2013. Incident patients (N=3,373) were followed for 2 years to identify their cART, categorized as single, double, triple or quadruple+ combination therapies. The treatment sequences were arranged according to a similarity criterion. It was performed by an Agglomerative Clustering algorithm configured with the Hamming distance and the Ward linkage method. The result is a graph containing one discretized timeline per patient. The lines were put in order one above the other and follow the 2-year follow-up. Finally, a smoothing was applied to the image to homogenize and highlight distinctive patterns. The combination of Agglomerative Clustering and of image processing forms an innovative methodology called TAK: Time-sequence Analysis through K-clustering. Over the 3,373 incident patients, 363 (11%) received no antivirals drugs during the follow-up, 385 (11%) were only treated after 6 months and 255 (8%) stopped cART at least one period during follow-up. 1,675 (50%) patients solely received a triple therapy over the follow-up, 484 (14%) changed at least once from a triple therapy to a single/double. In 2013, a triple therapy was the reference treatment and 30% of the patients had a 6-month period without any cART. This study validates the relevance of combining clustering methods with image processing to visualize the treatment sequences of a cohort in a clear and synthetic way.

A rapid method for screening and determination test of methanol content in ethanol-based products using portable Raman spectroscopy

Raman spectroscopy is nondestructive sample preparation and suitable for use in quality control-related field inspections, particularly on ethanol-based products. The influence of laser power density (PD) and exposure time (ET) on the Raman spectra similarity, specificity, selectivity, and accurate measurements for screening and determination of methanol content were carried out in this study.Raman spectra of samples were measured using a Raman handheld instrument. Measuring scales of PD and ET were varied from 50 to 300 milliWatt (mW) and 1000–5000 ms (ms), respectively. The similarity of Raman-spectra of those samples was statistically analysed with the help of hierarchical cluster analysis (HCA) and cross-correlation function (Ccorrel). The subtracted Raman spectra and pure ethanol spectra were implemented to identify methanol content in samples of ethanol-based products specifically.The Raman spectra measured at PD of 200–300 mW and clustered in one group by ward linkage method showed a similarity level of 99.98%. The variation of ET between 1000 and 5000 ms on fix PD of 300 mW showed high precision (RSD <2%) with more than 99% similarity of Raman spectra. Rinsing of PD was found to increase the signal: noise ratio, while prolonged exposure time reduced the signal: noise ratio. The precision and accuracy measurements were obtained by setting the PD and ET values of 300 mW and 4000 ms, respectively. The subtracted Raman spectra with the spectra of absolute ethanol introduced a simultaneous screening of methanol containing alcohols and determination of both substances.

Machine learning in the clinical and language characterisation of primary progressive aphasia variants

IntroductionPrimary progressive aphasia (PPA) is a clinical syndrome of neurodegenerative origin with 3 main variants: non-fluent, semantic, and logopenic. However, there is some controversy about the existence of additional subtypes. Our aim was to study the language and cognitive features associated with a new proposed classification for PPA. Material and methodsSixty-eight patients with PPA in early stages of the disease and 20 healthy controls were assessed with a comprehensive language and cognitive protocol. They were also evaluated with 18F-FDG positron emision tomography (PET). Patients were classified according to FDG PET regional metabolism, using our previously developed algorithm based on a hierarchical agglomerative cluster analysis with Ward's linkage method. Five variants were found, with both the non-fluent and logopenic variants being split into 2 subtypes. Machine learning techniques were used to predict each variant according to language assessment results. ResultsNon-fluent type 1 was associated with poorer performance in repetition of sentences and reading of irregular words than non-fluent type 2. Conversely, the second group showed a higher degree of apraxia of speech. Patients with logopenic variant type 1 performed more poorly on action naming than patients with logopenic type 2. Language assessments were predictive of PET-based subtypes in 86%–89% of cases using clustering analysis and principal components analysis. ConclusionsOur study supports the existence of 5 variants of PPA. These variants show some differences in language and FDG PET imaging characteristics. Machine learning algorithms using language test data were able to predict each of the 5 PPA variants with a relatively high degree of accuracy, and enable the possibility of automated, machine-aided diagnosis of PPA variants.